Mechanically Driven Bacteria‐Based Crack Detection
Bibliographic record
Abstract
Early detection of fatigue cracking is crucial to extend the life‐cycle of materials and structures. To reduce the risk of fatigue, parts are often over‐engineered or retired early, leading to material waste. Current methods for crack detection, including strain sensors or ultrasonic testing, can be costly, require regular maintenance, and do not respond to cracks directly via a repair mechanism. People are leveraging biology to create materials that can sense and respond. Engineered living materials have been primarily limited to porous matrices and hydrogels, which facilitate viability of organisms. We present an engineered living coating that can be applied to conventional structural materials to detect cracks. The coating integrates bacterial spores into a tailored synthetic matrix. This biohybrid coating approach unlocks potential, beyond crack detection, for crack mitigation through leveraging the biological component. This study: 1) describes the design of a spore‐polymer coating for in situ crack detection for structural materials and 2) demonstrates detection for different loading mechanisms, geometries, and materials. This work demonstrates how living materials can be used to enhance conventional materials and creates a valuable approach for crack detection. Our coating will reduce waste, increase product lifespan, and improve safety by preventing failure due to cracks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".